Cloud migration is essential for businesses seeking digital transformation and improved scalability, security, and operational efficiency. As global public cloud spending is projected to reach $675.4 billion in 2024, driven by generative AI (GenAI) and application modernisation, the need for efficient migration processes is more critical than ever. However, code refactoring remains a significant challenge, often hindering successful cloud adoption. This article delves into the obstacles of code refactoring and how leveraging GenAI through our Artemis AI can streamline this process while ensuring trust and security.
Challenges of Code Refactoring in Cloud Migration
Code refactoring is crucial for migrating applications and databases to the cloud, ensuring legacy code aligns with cloud-native features and optimises performance. Failing to refactor can result in increased downtime, higher maintenance costs, and an inability to fully leverage new technologies.
As cloud computing, microservices, and CI/CD pipelines become standard, the demand for enhanced software performance and reliability is at an all-time high. Manual code refactoring is no longer sustainable due to its complexity and time-intensive nature, necessitating extensive code review and rewriting by experts in the field. This manual approach introduces risks such as errors and performance issues, which can lead to delays in migration timelines and increased project costs. Projects relying on manual legacy code rewriting are six times more likely to encounter failure compared to those utilising automated conversion software.
Inefficient code refactoring significantly impacts cloud migration projects, contributing to higher costs and extended timelines. For instance, the process of refactoring a legacy VB6 application to achieve comparable functionalities took 3.5 years and cost over $750,000, underscoring the importance of efficient refactoring strategies in cloud migration.
Streamlining Code Refactoring with GenAI: Benefits and Risks
GenAI and large language models (LLMs) have the potential to streamline the code refactoring process. These advanced AI technologies can analyse vast codebases, identify areas that require optimisation, and suggest code modifications to improve cloud compatibility. As a result, businesses can expedite their cloud migration projects while minimising costs.
However, while GenAI and LLMs offer substantial benefits, they also come with certain drawbacks. As discussed in our previous blog, developing high-quality, high-performance software for intricate business scenarios is a massive challenge—one that often extends beyond the capabilities of LLMs alone. LLMs can struggle with context awareness, leading to suggestions that may not fully align with the specific requirements of a given project. Additionally, AI-generated code can introduce IP risks, or security issues that are not immediately apparent. Addressing these limitations is crucial for businesses aiming to leverage AI for automated code refactoring without compromising on quality or security.
Artemis AI: Derisking GenAI for Code Refactoring to Achieve Quality and Performance
Artemis AI is designed to harness the power of GenAI while mitigating the associated risks in software engineering. Drawing on a decade of TurinTech AI’s research in evolutionary optimisation, Artemis AI, our GenAI Code Optimiser, provides developers with a flexible framework that leverages LLMs and evolutionary optimisation, while also incorporating the critical thinking of human developers.
This approach allows businesses to fully capitalise on the benefits of GenAI in code refactoring with enhanced control and trust. It can significantly reduce cloud migration times from years to weeks, enabling developers to focus on innovation.
Key features include:
- Code Analysis: Identify code snippets and opportunities for refactoring (e.g., duplicated code, complex logic) in seconds, not hours.
- LLM-based Recommendations: Use a mix of LLMs to find the optimal recommendations for context-aware refactoring.
- Scoring Code Suggestions: Evaluate the quality of LLM-based code suggestions based on performance, security, stability, and runtime.
- Validation Checks: Conduct rigorous validation, including compilation tests, unit tests, security assessments, and IP reviews.
- Performance Optimisation: Optimise code for microservices architecture to ensure optimal cloud performance.
- Code Chat: Ask code-related questions, seek clarification on suggested refactoring changes, receive explanations, and more.
Automated Code Refactoring with Artemis AI
Accelerate Cloud Migration with Artemis AI
Artemis AI can refactor popular programming languages such as SQL, C#, C++, Q, Java, Python, and JavaScript, enhancing their performance, maintainability, and compatibility with modern standards and technologies. It also works smoothly with major data and cloud platforms, including DataBricks, Snowflake, AWS, Azure, Google Cloud, and on-premises systems.
The benefits of Artemis AI include improved application scalability and performance, reduced migration costs and technical debt, decreased carbon footprint and energy consumption, and minimised manual effort and modification risks. By leveraging LLMs for code refactoring through Artemis AI, organisations can achieve more efficient, cost-effective, and secure cloud migrations, ultimately enhancing their competitive edge in the digital landscape.
